Explainable and Faithful Educational Recommendations through Causal Language Modelling via Knowledge Graphs

Abstract

The rapid expansion of digital education has significantly increased the need for recommender systems to help learners navigate the extensive variety of available learning resources. Recent advancements in these systems have notably improved the personalization of course recommendations. However, many existing systems fail to provide clear explanations for their recommendations, making it difficult for learners to understand why a particular suggestion was made. Researchers have emphasized the importance of explanations in various domains such as e-commerce, media, and entertainment, demonstrating how explanations can enhance system transparency, foster user trust, and improve decision-making processes. Despite these insights, such approaches have been rarely applied to the educational domain, and their effectiveness in practical use remains largely unexamined. My research focuses on developing explainable recommender systems for digital education. First, I aim to design knowledge graphs that can support high-quality recommendations in the educational context. Second, I will create models backed by these knowledge graphs that not only deliver accurate recommendations but also provide faithful explanations for each suggestion. Third, I will evaluate the effectiveness of these explainable recommender systems in real-world educational environments. Ultimately, this research aims to advance the development of more transparent and user-centric educational technologies.